search for: lognormals

Hi, Does anyone know what the mean value of a lognormal distribution in base-2 is? I am simulating stochastic population growth and if I were working in base-e, I would do:lambda <- 1.1 #multiplicative growth rates <- 0.6 #stochasticity (std. dev)lognormal <- rlnorm(100000, log(lambda) - (s^2)/2, s)## or lognormal <- exp( rnorm( 100000, log(lambda) - (s^2)/2,

Dear List, I have read that a lognormal mixture model having a pdf of the form f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1 and f2 are lognormal distributions. Any pointers on how to create a function that would produce the 5 parameters of f(x) would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os

Hi R lovers! I'd like to know how to use the parameter 'start' in the function fitdistr() obviously I have to provide the initial value of the parameter to optimize except in the case of a certain set of given distribution Indeed according to the help file for fitdistr " For the following named distributions, reasonable starting values will be computed if `start'

Dear all, I want to compare some different models for a dataset by QQ plots and AIC. I get the following AICs: - linear model: 19759.66 - GAMLSS model: 18702.7 - linear model with lognormal response: -7862.182 The QQ plots show that the lognormal model fits better than the linear model, but still much worse than the GAMLSS. So, in my opinion, the AIC of the lognormal model should be between the

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a

Dear, I'm very new to R Gui and I have to make an assignment on Gamma Regressions. Surfing on the web doesn't help me very much so i hope this forum may be a step forward. The question sounds as follows: The data set is in the library MASS first install library(MASS) then type data(mammals) attach(mammals) Assignment: Fit the gamma model and lognormal model for the mammals data.

Hi, What does it mean to say Lognormal distribution with a mean of 1.03E-6 with a range factor of 100 ? How can I find the lognormal distribution paramters from this information? Thanks, Tims [[alternative HTML version deleted]]

Hi, I am beginner with R and need firm guidance with my problem. I have seen some other threads discussing the subject of right censored data, but I am not sure whether or not this problem can be regarded as such. Data: I have a vector with laboratory test data (strength of wood specimens, example attached as txt-file). This data is the full sample. It is a common view that this kind of data

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

I am trying to plot a lognormal density curve on top of an existing histogram. Can anybody suggest a simple way to do this? Even if someone could just explain how to plot a regular normal density curve on top of an existing histogram, it would be a big help. Also, is there some way to search through the R-help archives other than simple browsing? Thank you so much. Your help and time is greatly

I am trying to fit a lognormal distribution to a set of data and test its goodness of fit with regard to predicted values. I managed to get so far: > y <- c(2,6,2,3,6,7,6,10,11,6,12,9,15,11,15,8,9,12,6,5) > library(MASS) > fitdistr(y,"lognormal",start=list(meanlog=0.1,sdlog=0.1)) meanlog sdlog 1.94810515 0.57091032 (0.12765945) (0.09034437) But I would

Hi, I am trying to learn lognormal mixture models with EM. I was wondering how does one compute the log likelihood. The current implementation I have is as follows, which perform really bad in learning the mixture models. __BEGIN__ # compute probably density of lognormal. dens <- function(lambda, theta, k){ temp<-NULL meanl=theta[1:k] sdl=theta[(k+1):(2*k)]

Hi R Users, I'm going to estimate via. ML the parameters in Poisson Lognormal model. The model is: x | lambda ~ Poisson(lambda) lambda ~ Lognormal(a,b) Unfortunately, I haven't found a useful package allowing for such estimation. I tried to use "poilog" package, but there is no equations and it's hard to understand what exactly this package really does. Using it I get the

Full_Name: Anthony Gichangi Version: 1.90 OS: Windows XP Pro Submission from: (NULL) (130.225.131.206) The function rlnorm generates negative values for lognormal distribution. x- rlnorm(1000, meanlog = 0.6931472, sdlog = 1) Regards Anthony

Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

Hi R lovers Here is a numerical vector test > test [1] 206 53 124 112 92 77 118 75 48 176 90 74 107 126 99 84 114 147 99 114 99 84 99 99 99 99 99 104 1 159 100 53 [33] 132 82 85 106 136 99 110 82 99 99 89 107 99 68 130 99 99 110 99 95 153 93 136 51 103 95 99 72 99 50 110 37 [65] 102 104 92 90 94 99 76 81 109 91 98 96 104 104 93 99 125 89

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile